[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117616-en":3,"doc-seo-117616-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117616,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Sentiment Analysis Using Machine Learning - A Comparative Study","Sentiment Analysis Using Machine Learning - A Comparative Study examines how sentiment analysis on social media has evolved alongside the rapid growth of online platforms such as Facebook, Twitter, and blogs. It reviews the state of the art by outlining sentiment analysis types, methodologies, applications, challenges, and the comparative performance of different machine learning approaches. Evaluation across classifiers highlights logistic regression as achieving the best results, while emphasizing the need for easier, more versatile and practical future methods with improved algorithm performance.","ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 12 N. 1 (2023), e26785  \neISSN: 2255-2863  \nDOI: [https://doi.org/10.14201/adcaij.26785](https://doi.org/10.14201/adcaij.26785)  \nSentiment Analysis Using Machine Learning: A Comparative Study  \nNeha Singh and Umesh Chandra Jaiswal Department of ITCA, MMMUT, Gorakhpur, India, 273010. [nehitca@mmmut.ac.in](nehitca@mmmut.ac.in), [ucjitca@mmmut.ac.in](ucjitca@mmmut.ac.in)  \nKEYWORDS ABSTRACT  \n\n| sentiment analysis; machine learning; social media; logistic regression | In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, thereis still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’performance. |\n| --- | --- |\n| 1. Introduction\u003Cbr>The popularity of rapidly expanding online social networks that rely on electronic media has encouraged new researchers to continue their work on sentiment analysis (SA) . Internet-based specialist organizations survey information on websites, online gatherings, reviews, tweets, and item audits. Opinion research has applications in a variety of fields, including party planning, political decision-making, medical care monitoring, sales, and mindfulness administration (J. Singh et al., 2017). Sentiment analysis provides key insights into people’s opinions, aiding decision-makers in making a choice on behalf of another user (Jagdale et al., 2019). There are numerous methods for sentiment analysis, including machine learning, dictionary-based, watchword-based, and idea-based methodologies (Qazi et al., 2017) .\u003Cbr>Sentiment analysis is a “suitcase” research problem that needs to be handled by numerous natural language processing subtasks, including angle extraction, subjectivity identification, named substance, |  |\n\nNeha Singh and Umesh Chandra Jaiswal Sentiment Analysis Using Machine Learning: A Comparative Study  \nADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal  \nRegular Issue, Vol. 12 N. 1 (2023), e26785 eISSN: 2255-2863-[https://adcaij.usal.es](https://adcaij.usal.es)[ ](https://adcaij.usal.es)Ediciones Universidad de Salamanca-cc by-nc-nd  \nand mockery place. The principal objective of sentiment analysis is to separate emotions from other elements (such as items or administrations) to produce helpful data. SA is done over an immense assortment of general suppositions, which could be of various sorts. Sentiment analysis includes deciding the evaluative idea of a piece of text. For instance, an item survey can communicate positive, negative, or nonpartisan sentiments. Over the previous decade, there has been a considerable development in the publication of micro-content on blogging websites, for example, Twitter, and social media overall (Raghuvanshi & Patil, 2016) . It is utilised to explain unbiased assessments, negative or positive sentiments,","cbCaii4O5udwz8bG","https://ap.wps.com/l/cbCaii4O5udwz8bG","pdf",978135,1,15,"English","en",105,"# Introduction\n## Sentiment analysis methods and applications\n## Challenges and opinion mining context","[{\"question\":\"What is the main focus of sentiment analysis in this study?\",\"answer\":\"The study focuses on separating emotions from other text elements to obtain useful data, including determining evaluative sentiment such as positive, negative, or neutral.\"},{\"question\":\"Which machine learning model is reported as achieving the best performance?\",\"answer\":\"The paper reports logistic regression as the model that achieves the best result during performance evaluation across different classifiers.\"},{\"question\":\"What types of sentiment analysis approaches does the paper review?\",\"answer\":\"It reviews sentiment analysis types and compares multiple machine learning procedures, alongside discussing different methodologies, applications, challenges, and evaluation parameters.\"}]","Sentiment Analysis Using Machine Learning - 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